CNN Remote Sensing and Satellite Image Analysis

• Author(s): Rajat Yadav ; Raghav Kumar ; Aniket Tripathi
• Paper ID: 1709002
• Page: 174-185
• Published Date: 31-12-2020
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 4 Issue 6 December-2020

Abstract

CNNs have made it possible for remote sensing to process data more accurately and without manual effort which is especially beneficial for satellite images. Rather than manually including handmade features, CNNs find patterns in the raw data and perform better and more easily on large and complicated data. It is especially helpful in spotting changes in land use, deforestation, new urban growth and harm to the environment. Many remote sensing applications, include land cover classification, detecting objects, examining plants and finding change over time, have shown that CNNs perform very well. Tasks like urban planning and precision farming depend on spatial precision which is offered by advanced CNN architectures like U-Net, ResNet and DeepLab. They can handle data gathered with various approaches, including optical, SAR and LiDAR sensors, giving a broader view of the environment. Even though CNNs work well, they also have some limitations. Labels on a large dataset are still quite hard to get, making it difficult for supervised learning. Making these datasets needs lots of resources, mainly in places that are seldom observed. In order to resolve this, researchers rely on transfer learning by using ImageNet trained models and adjusting them for their particular tasks. Some are investigating self-supervised and weakly supervised learning to require less labeling data. A further issue is that deep CNN models require a lot of computing resources for both training and deployment. It prevents some smaller institutions from using AI solutions. Therefore, new approaches and techniques to make models as lightweight as possible are being developed. In addition, explainability is still a main problem since CNNs are often difficult to explain how they work. Using tools such as Grad-CAM and saliency maps can explain why a model made certain decisions which builds confidence. Right now, CNNs have many uses in environmental monitoring, though ongoing efforts are needed to deal with existing issues.

Keywords

Convolutional Neural Networks, Remote Sensing, Satellite Imagery, Image Classification, Deep Learning, Object Detection, Semantic Segmentation, Transfer Learning, Data Fusion, Environmental Monitoring

Citations

IRE Journals:
Rajat Yadav , Raghav Kumar , Aniket Tripathi "CNN Remote Sensing and Satellite Image Analysis" Iconic Research And Engineering Journals Volume 4 Issue 6 2020 Page 174-185

IEEE:
Rajat Yadav , Raghav Kumar , Aniket Tripathi "CNN Remote Sensing and Satellite Image Analysis" Iconic Research And Engineering Journals, 4(6)